scholarly journals In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Wen-Fan Chen ◽  
Hsin-You Ou ◽  
Keng-Hao Liu ◽  
Zhi-Yun Li ◽  
Chien-Chang Liao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Standard Of Care ◽  
Clinical Decision ◽  
Liver Lesion ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Automatic Interpretation ◽  
In Series ◽  
High Expenditure ◽  
Liver Hepatocellular Carcinoma

Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.

CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15623-e15623 ◽  
Author(s):  
Derek L West ◽  
Aikaterini Kotrotsou ◽  
Andrew Scott Niekamp ◽  
Tagwa Idris ◽  
Dunia Giniebra Camejo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Texture Features ◽  
Ct Images ◽  
The Cancer Genome Atlas ◽  
Tumor Segmentation ◽  
Full Potential ◽  
Imaging Features ◽  
Treatment Naïve

e15623 Background: The utilization of computed tomography (CT) has virtually replaced the need for tissue diagnosis in hepatocellular carcinoma (HCC). Imaging features (e.g. size, shape and vascularity) have been associated with patient survival. However, the full potential of CT in HCC diagnosis may not be reached, as high-throughput computing allows for extraction of quantitative features that are not part of radiologists’ lexicon. The purpose of this study was to investigate the ability of radiomic analysis to successfully identify specific doxorubicin chemoresistant genes on CT images of treatment-naïve hepatocellular carcinoma (HCC). Methods: We identified 27 treatment-naïve patients with a single HCC tumor from The Cancer Genome Atlas (TCGA) whom had gene expression profiles. Baseline CT images were obtained from The Cancer Imaging Archive (TCIA). 3D Slicer software was used for manual tumor segmentation and final segmented images were reviewed by a board-certified radiologist. Following tumor segmentation, texture analysis was performed on MATLAB environment. A total of 310 rotation invariant texture features, which measure tumor heterogeneity, were obtained (first-order histogram and grey level co-occurrence matrix). The mRMR method was used to select the most relevant radiomic features. ROC analysis and LOOCV were used to assess the performance of five specific genes known to confer doxorubicin chemoresistance (TP53, TOP2A, CTNNB1, CDKN2A and AKT1). Results: Radiomic analysis identified TP53 (AUC = 86.61%, Specificity = 92.31%, Sensitivity = 92.9%), TOP2A (AUC = 78.0%, Specificity = 69%, Sensitivity = 85.7%), CTNNB1 (AUC = 86.8%, Specificity = 92.3%, Sensitivity = 85.7%), CDKN2A (AUC = 76.9%, Specificity = 76.9%, Sensitivity = 78.6%) and AKT1 (AUC = 72.5%, Specificity = 69.2%, Sensitivity = 85.7%) in treatment-naïve HCC CT studies. Conclusions: The identification of specific genes that confer chemoresistance to doxorubicin can be reliably ascertained via the use of radiomic analysis. This study may help tailor future treatment paradigms via the ability to categorize HCC tumors on genetic level and identify tumors which may not have a favorable response to doxorubicin based therapies.

scholarly journals Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

2015 ◽  
Vol 2 (4) ◽  
pp. 041011 ◽  
Author(s):  
Sebastian Echegaray ◽  
Olivier Gevaert ◽  
Rajesh Shah ◽  
Aya Kamaya ◽  
John Louie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pilot Study ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Core Samples

scholarly journals Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 397
Author(s):  
Corina Radu ◽  
Petra Fisher ◽  
Delia Mitrea ◽  
Iosif Birlescu ◽  
Tiberiu Marita ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Image Fusion ◽  
Real Time ◽  
Detection System ◽  
Ct Images ◽  
Robotic System ◽  
Targeted Treatment ◽  
Tumor Segmentation ◽  
Tumor Visualization ◽  
Contrast Enhanced Ct

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.

Biomarker Identification for Liver Hepatocellular Carcinoma and Cholangiocarcinoma Based on Gene Regulatory Network Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Qiuyan Huo ◽  
Yuying Ma ◽  
Yu Yin ◽  
Guimin Qin
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regulatory Networks ◽  
Primary Liver Cancer ◽  
Endocrine Resistance ◽  
Molecular Characteristics ◽  
Network Clustering ◽  
Resistance Pathway ◽  
Gene Regulatory ◽  
Tf Gene ◽  
Liver Hepatocellular Carcinoma

Aims: We aimed to find common and distinct molecular characteristics between LIHC and CHOL based on miRNA-TF-gene FFL. Background: Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are two main histological subtypes of primary liver cancer with a unified molecular landscape, and feed-forward loops (FFLs) have been shown to be relevant in these complex diseases. Objective: To date, there has been no comparative analysis of the pathogenesis of LIHC and CHOL based on regulatory relationships. Therefore, we investigated the common and distinct regulatory properties of LIHC and CHOL in terms of gene regulatory networks. Method: Based on identified FFLs and an analysis of pathway enrichment, we constructed pathway-specific co-expression networks and further predicted biomarkers for these cancers by network clustering. Resul: We identified 20 and 36 candidate genes for LIHC and CHOL, respectively. The literature from PubMed supports the reliability of our results. Conclusion: Our results indicated that the hsa01522-Endocrine resistance pathway was associated with both LIHC and CHOL. Additionally, six genes (SPARC, CTHRC1, COL4A1, EDIL3, LAMA4 and OLFML2B) were predicted to be highly associated with both cancers, of which SPARC was significantly highly ranked. Other: In addition, we inferred that the Collagen gene family, which appeared more frequently in our overall prediction results, might be closely related to cancer development.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Li ◽  
Chen Zhu ◽  
Peipei Yue ◽  
Tianyu Zheng ◽  
Yan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Digestive System ◽  
Prognostic Significance ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
System Structure ◽  
Liver Hepatocellular Carcinoma

Abstract Background Abnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms. Methods Gene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently. Results High glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop. Conclusions Our results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism–signaling target combined therapy.

scholarly journals Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 790
Author(s):  
Monica Lupsor-Platon ◽  
Teodora Serban ◽  
Alexandra Iulia Silion ◽  
George Razvan Tirpe ◽  
Alexandru Tirpe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hepatocellular Carcinoma ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Liver Lesion ◽  
Focal Liver Lesion ◽  
Ultrasound Contrast Agents ◽  
Alcoholic Fatty Liver ◽  
Alcoholic Fatty Liver Disease

Global statistics show an increasing percentage of patients that develop non-alcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma (HCC), even in the absence of cirrhosis. In the present review, we analyzed the diagnostic performance of ultrasonography (US) in the non-invasive evaluation of NAFLD and NAFLD-related HCC, as well as possibilities of optimizing US diagnosis with the help of artificial intelligence (AI) assistance. To date, US is the first-line examination recommended in the screening of patients with clinical suspicion of NAFLD, as it is readily available and leads to a better disease-specific surveillance. However, the conventional US presents limitations that significantly hamper its applicability in quantifying NAFLD and accurately characterizing a given focal liver lesion (FLL). Ultrasound contrast agents (UCAs) are an essential add-on to the conventional B-mode US and to the Doppler US that further empower this method, allowing the evaluation of the enhancement properties and the vascular architecture of FLLs, in comparison to the background parenchyma. The current paper also explores the new universe of AI and the various implications of deep learning algorithms in the evaluation of NAFLD and NAFLD-related HCC through US methods, concluding that it could potentially be a game changer for patient care.

scholarly journals Immunotherapy in hepatocellular carcinoma: evaluation and management of adverse events associated with atezolizumab plus bevacizumab

2021 ◽  
Vol 13 ◽  
pp. 175883592110311
Author(s):  
Chiun Hsu ◽  
Lorenza Rimassa ◽  
Hui-Chuan Sun ◽  
Arndt Vogel ◽  
Ahmed O. Kaseb
Keyword(s):  
Hepatocellular Carcinoma ◽  
Adverse Events ◽  
Kinase Inhibitor ◽  
Treatment Options ◽  
Healthcare Providers ◽  
Safety Data ◽  
Standard Of Care ◽  
Antiangiogenic Agents ◽  
Efficacy And Safety ◽  
First Line

In light of positive efficacy and safety findings from the IMbrave150 trial of atezolizumab plus bevacizumab, this novel combination has become the preferred first-line standard of care for patients with unresectable hepatocellular carcinoma (HCC). Several additional trials are ongoing that combine an immune checkpoint inhibitor with another agent such as a multiple kinase inhibitor or antiangiogenic agent. Therefore, the range of first-line treatment options for unresectable HCC is likely to increase, and healthcare providers need succinct information about the use of such combinations, including their efficacy and key aspects of their safety profiles. Here, we review efficacy and safety data on combination immunotherapies and offer guidance on monitoring and managing adverse events, especially those associated with atezolizumab plus bevacizumab. Because of their underlying liver disease and high likelihood of portal hypertension, patients with unresectable HCC are at particular risk of gastrointestinal bleeding, and this risk may be exacerbated by treatments that include antiangiogenic agents. Healthcare providers also need to be alert to the risks of proteinuria and hypertension, colitis, hepatitis, and reactivation of hepatitis B or C virus infection. They should also be aware of the possibility of rarer but potentially life-threatening adverse events such as pneumonitis and cardiovascular events. Awareness of the risks associated with these therapies and knowledge of adverse event monitoring and management will become increasingly important as the therapeutic range broadens in unresectable HCC.

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